[1]李燕燕,杨昊天,曾玙璠.基于随机森林多目标粒子群算法的城市最优资本结构分析[J].郑州大学学报(工学版),2019,40(04):14.
 Urban Optimal Capital Structure Analysis based on Random Forest and Multi-objective Particle Swarm Optimization[J].Journal of Zhengzhou University (Engineering Science),2019,40(04):14.
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基于随机森林多目标粒子群算法的城市最优资本结构分析()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
40
期数:
2019年04期
页码:
14
栏目:
出版日期:
2019-07-10

文章信息/Info

Title:
Urban Optimal Capital Structure Analysis based on Random Forest and Multi-objective Particle Swarm Optimization
作者:
李燕燕杨昊天曾玙璠
文献标志码:
A
摘要:
城市资本结构是一个受到多因素交互影响的复杂问题.本文试图基于随机森林多目标粒子群算法构建多目标多因素影响下的城市最优资本结构模型,对城市资本结构状况进行剖析.首先利用随机森林的拟合回归特性,对历史数据进行拟合,从中找到历史数据特征之间的关系.随后采用多目标粒子群约束优化算法,根据已有的关系特征去寻找使目标同时达到最好效果的特征值,再根据这些效果最好的特征值从历史数据中寻找相关性最高的数据,从而分析出资本结构配置相对较优的城市以及年份.通过不断学习这些较优的结构配置,可以对各个城市的发展起到良好的借鉴作用
Abstract:
Urban capital structure was a complex?problem affected by multi-factors and multi-objective particle.This paper attempt ed to explore a scientific and appropriate d algorithm to construct the optimal capital structure model under the influence of multi-objective and multi-factors to analyze the situation of urban capital structure.First, the data in history could find the relationship among features of the data in history by using the regression characteristics of random forest. Then, the multi-objective particle swarm optimization algorithm was used to find values of the features that achieve the best results according to the existing relationship features. Then finding the most correlate data from the historical data based on the best eigenvalues of these effects. Therefore, the cities and the years with relatively better capital structure allocations are analyzed. We could play a good role in the reference and development of each city by continuously learning these superior structural configurations
更新日期/Last Update: 2019-07-29